Matan Atzmon, Koki Nagano, Sanja Fidler, Sameh Khamis, and Yaron Lipman. Frame averaging for equivariant shape space learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 631-641, 2022.
Albert-László Barabási and Réka Albert. Emergence of scaling in random networks. In Science, pages 509-512. American Association for the Advancement of Science, 1999.
Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373-1396, 2003.
Xavier Bresson and Thomas Laurent. Residual gated graph ConvNets. In preprint arXiv:1711.07553, 2017.
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Velickovic. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. preprint arXiv:2104.13478, 2021.
Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M Bronstein, and Max Hansmire. Graph neural networks for link prediction with subgraph sketching. In International Conference on Learning Representations (ICLR), 2023.
Dexiong Chen, Leslie O'Bray, and Karsten Borgwardt. Structure-aware transformer for graph representation learning. In International Conference on Machine Learning (ICML), pages 3469-3489. PMLR, 2022.
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna. Can graph neural networks count substructures? In Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 10383-10395, 2020.
Fan Chung. Spectral graph theory. American Mathematical Society, 1997.
Taco Cohen and Max Welling. Group equivariant convolutional networks. In International Conference on Machine Learning (ICML), pages 2990-2999. PMLR, 2016.
Taco S. Cohen and Max Welling. Steerable CNNs. In International Conference on Learning Representations (ICLR), 2017.
Leonardo Cotta, Beatrice Bevilacqua, Nesreen Ahmed, and Bruno Ribeiro. Causal lifting and link prediction. preprint arXiv:2302.01198, 2023.
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas J Guibas. Vector neurons: A general framework for SO(3)-equivariant networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 12200-12209, 2021.
Vijay Prakash Dwivedi, Chaitanya K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. Benchmarking graph neural networks. In JMLR, 2022a.
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. Graph neural networks with learnable structural and positional representations. In International Conference on Learning Representations (ICLR), volume 10, 2022b.
Nadav Dym and Steven J Gortler. Low dimensional invariant embeddings for universal geometric learning. preprint arXiv:2205.02956, 2022.
Nadav Dym and Haggai Maron. On the universality of rotation equivariant point cloud networks. In International Conference on Learning Representations (ICLR), 2021.
Paul Erdős, Alfred Rényi, et al. On the evolution of random graphs. Publ. Mathematical Institute Hungarian Academy of Science, 5(1):17-60, 1960.
Marc Finzi, Max Welling, and Andrew Gordon Wilson. A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups. In International Conference on Machine Learning (ICML), pages 3318-3328. PMLR, 2021.